9.3.2 Policies

A policy specifies what the agent should
do under all contingencies. An agent wants to find an optimal policy - one that maximizes its expected utility.

A policy consists of a decision function for each decision variable. A
decision function for a decision variable is a function that specifies a value for
the decision variable for each assignment of values of its parents.
Thus, a policy specifies what the agent will do for each possible
value that it could sense.

There are eight different policies, because there are three possible forecasts and
there are two choices for each of the forecasts.

Example 9.15:
In Example 9.13, a policy specifies a decision function
for CheckSmoke and a decision function for Call. Some of the
policies are

Never check for smoke, and call only if there is a report.

Always check for smoke, and call only if it sees smoke.

Check for smoke if there is a report, and call only if there is a
report and it sees smoke.

Check for smoke if there is no report, and call when it does not see
smoke.

Always check for smoke and never call.

In this example, there are 1,024 different policies (given that each
variable is binary). There are 4 decision functions for
CheckSmoke. There are 28 decision functions for Call; for each
of the 8 assignments of values to the parents of Call, the agent can
choose to call or not.